# Quickstart
**There are four basic steps to build an end-to-end flow on Context Data**
1\). Source Connection: Build connection(s) to where your source data resides (e.g. MySQL, PostgreSQL, Amazon S3)
2\). Embedding Model: Create a link to the embedding model which will convert data retrieved from the source to vector embeddings (basically an array of numbers)
3\). Target Connection: Build connection(s) to where the vector embeddings will be saved (and where your AI application will read from)
4\). Flow: The flow ties of the steps above (source connection, embedding model and target connection) into an end-to-end process ready to be executed.
Basically, when a flow is triggered, it will:
* Get the data from the source connection that you defined
* Convert the retrieved data to a format optimized for vector search
* Write the converted data to the vector database/store
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